The Virtual Debt Factory: Towards an Analysis of Debt and Abstraction in the American Credit Crisis
Bibliographic record
Abstract
Emanating from the United States, the ongoing global credit crisis has provided important insights into a shady new area of capitalist exploitation: the consumer debt factory. In an effort to speed up and quantifiably increase the circulation of consumer credit to match the consumption needs of post-Fordist accumulation, this industry—comprising financial institutions, consumer database companies, and credit rating agencies—has created a highly detailed body of information to stand-in for the corporeal self. This paper therefore examines this industry’s conceptualization of the self as a disembodied mechanism for mass-producing debt, creating a highly volatile informational commodity divorced from all material constraints. In using the credit crisis as a focal point, this paper considers how the far-reaching credit apparatus at the heart of the debt factory gives rise to the fatal abstractions that support, and ultimately undermine, contemporary capitalist economies. By substituting data for flesh, the credit industry has created an antagonism between the material and informational forms of the self, resulting in the construction of a virtual debtors prison. The ensuing analysis will highlight both the exploitative nature of this bifurcation as well as its profound contradictions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".